报告人简介：
张东松，美国亚利桑那大学Eller威廉希尔管理信息系统博士，现为美国马里兰大学巴尔的摩郡校区信息系统系终身正教授，主要从事电子商务与商务智能，社交网络、人机交互及应用研究。发表学术论文140 余篇，其中50篇被SCI/SSCI期刊收录，包括MIS Quarterly，Journal of Management Information Systems，and IEEE Transactions on Knowledge and Data Engineering，IEEE Transactions on Software Engineering等，已获得美国国家科学基金委、美国国家健康研究院、美国国家教育部、美国福特科研基金会、中国国家自然科学基金、中国科学院和谷歌公司等十多项科研项目资助。他曾经担任多个国际学术会议的会议主席或程序委员会主席。
报告简介：
The value and credibility of online consumer reviews are compromised by significantly increasing yet difficult-to-identify fake reviews. Extant models for automated online fake review detection rely heavily on verbal behaviors of reviewers while largely ignoring their nonverbal behaviors. This research identifies a variety of nonverbal behavioral features of online reviewers and examines their relative importance for the detection of fake reviews in comparison to that of verbal behavioral features. The results of an empirical evaluation using real-world online reviews reveal that incorporating nonverbal features of reviewers can significantly improve the performance of online fake review detection models. Moreover, compared with verbal features, nonverbal features of reviewers are shown to be more important for fake review detection. Furthermore, model pruning based on a sensitivity analysis improves the parsimony of the developed fake review detection model.
